Cytotoxicity of carbon nanotube variants: A comparative<i>in vitro</i>exposure study with A549 epithelial and J774 macrophage cells
Bibliographic record
Abstract
While production of engineered carbon nanotubes (CNTs) has escalated in recent years, knowledge of risk associated with exposure to these materials remains unclear. We report on the cytotoxicity of four CNT variants in human lung epithelial cells (A549) and murine macrophages (J774). Morphology, metal content, aggregation/agglomeration state, pore volume, surface area and modifications were determined for the pristine and oxidized single-walled (SW) and multi-walled (MW) CNTs. Cytotoxicity was evaluated by cellular ATP content, BrdU incorporation, lactate dehydrogenase (LDH) release, and CellTiter-Blue (CTB) reduction assays. All CNTs were more cytotoxic than respirable TiO2 and SiO2 reference particles. Oxidation of CNTs removed most metallic impurities but introduced surface polar functionalities. Although slopes of fold changes for cytotoxicity endpoints were steeper with J774 compared to A549 cells, CNT cytotoxicity ranking in both cell types was assay-dependent. Based on CTB reduction and BrdU incorporation, the cytotoxicity of the polar oxidized CNTs was higher compared to the pristine CNTs. In contrast, pristine CNTs were more cytotoxic than oxidized CNTs when assessed for cellular ATP and LDH. Correlation analyses between CNTs' physico-chemical properties and average relative potency revealed the impact of metal content and surface area on the potency values estimated using ATP and LDH assays, while surface polarity affected the potency values estimated from CTB and BrdU assays. We show that in order to reliably estimate the risk posed by these materials, in vitro toxicity assessment of CNTs should be conducted with well characterized materials, in multiple cellular models using several cytotoxicity assays that report on distinct cellular processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".